Developing A Framework for Management Decision Making for the Tasmanian Sand Flathead Fishery

Disclaimer

The following work is preliminary and intended only as tool for eliciting feedback on data, modelling and other aspects of these fisheries.

None of these results are final.

These analyses do not necessarily reflect the point of view of IMAS or other funders and in no way anticipate future policy in this area.

Objective

Develop an MSE framework for the Tasmanian Sand Flathead fishery that can inform management decision making including research prioritization, assessment methodology, specification of fishing regulations and enforcement.

Project details

‘Development of a draft operating model in openMSE for the assessment and management strategy evaluation of Southern Sand Flathead in Tasmania.’

Term	15/03/2024 - 1/7/2024
Funding body	University of Tasmania
Funding stream	Subcontract
Project No.	T0030292
Project Partners	IMAS, Blue Matter Science Ltd.
Blue Matter Team	Drs. Tom Carruthers & Adrian Hordyk
IMAS Principal Investigators	Dr. Sean Tracey

Current Issues

Tabulated below are a list of current issues / assumptions that should be addressed at the current stage of framework development.

Issue	Notes
Length at 50% maturity, and logistic slope	From an online report by NRE (‘around 27 cm’) - but actually there is GonadState, Gonadweightand Statge Mature 3-7, in the historical data (how to interpret these?). StageMature 3-7 is 50% at length 31cm in the data (aggregated)
Selectivity of largest / oldest fish	Currently assumed to be flat-topped, asymptotic.
Natural mortality rate	Using value of 0.28 from Kruek et al. 2023 but what is the origin of this?
Minimum size limit of 32cm	Is this correct, for all areas?
Background rate of discarding	Assumed to be zero - but is this accurate? We can do bag limit modelling but need CPUE vs release rate by trip (baglimit)
Observation error a placeholder to get demo MPs working	Later these observation processes can be characterized statistically
Observation biases assumed to be nil	For now, observed catches, indices etc are assumed to be unbiased and not hyperstable or hyperdeplete
Implementation assumed to be perfect	For now, for demonstration purposes, any management advice is assumed to be followed exactly
Nominal Effort could be improved as an index of exploitation rate	Can we derive effort / habitat area. There is the potential to borrow information on catchability among areas/models - priors, metaanalysis, EM.
Catches are expanded to totals using expansion factor - no uncertainty	How can we get observation error in total catches? How are expansion factors calculated - can we do bootstrapping etc?
Discard mortality rate assumed to be 9% but from a study elsehwere	Lyle et al. 2006. This is used to include discard mortality in total catch data (in model conditioning [Catch = ExpFac x (Kept + Rel * DiscMort)] and used in projections that would affect any kind of regulation affecting discarding such as size limits, bag limits etc.
Total recreational effort	currently calculated by Duration_hrs x Npersons x ExpWt (what is the ‘expansion factor’??)
rec_suvey_data.xls sheet 2017-18 has DurationHrs formatted as a date	I manually changed this to ‘number’ format.
Recreational survey index by large region is standardized as a linear model	log(CPUE) ~ Yr + Quarter + Region + WaterBody + Type (Large region is something like SEC, region is something like Tasman, Derwent esturary etc)
Not clear how to assign calendar year to commercial year	Currently this is assumed to occur mostly in the second half, ie Nov 1 - Sep 1, so 2022/23 would be assigned the year 2023.
Region’ is inconsistent among datasets current assumptions are to group by large region (Lregion) and Region:
Large region (LRegion)	Region
SEC	Derwent Estuary, Tasman, Frederick Henry/Norfolk Bay, South-eastern coast, D’entrecasteaux Channel, South, Northwest Bay, SECest, SEC
EC	Great Oyster Bay, Central-eastern coast, Eastern coast, Coles Bay, Georges Bay, EC
NWC	North-western coast, King Island, rocky cape, NWC
NEC	Tamar River, North-eastern coast, Flinders Island, Spring Bay, Flinders/Eastcoast, NC, EC, Deal island, Hogan group, NEC, FI
WC	Central-western coast, Western coast, South-western coast
unknown	EAT, ECS, ET, SET, CBS, no sample

Study Area

Figure 1. Study area. Area definitions (top left), areas of high recreational effort (top middle), areas of research focus (top right), commercial effort distribution (bottom left), commercial catch per unit effort (bottom right).

Operating models

Stochasticity in life history characteristics

In order to include plausible uncertainty in the life-history dynamics for sand flathead, frequentist models of somatic growth and the length-weight relationship were fitted to data and parameter values draw from the variance covariance matrix arising from those fits (Figures 2 and 3)

Figure 2. Generation of stochastic life history parameters for a preliminary operating model for Flinders Island. Top left is the fit of a preliminary von Bertalanffy somatic growth model to observed age-length data. Top right is the correlation among simulated asympotic length (Linf) and maximum growth rate (K) parameters drawn from the variance-covariance matrix of the model fit. Bottom left is the simualted natual mortality rate (M) given a fixed ratio of M/K and a lognormal error with CV of 10%. Bottom right is the simulated length at 50% maturity (L50) given a fixed ratio of L50/Linf and a lognormal error with CV of 10%.

Figure 3. Generation of stochastic weight-length parameters for a preliminary operating model for Flinders Island. Top left is the fit of a preliminary weight length (W=aL^b) growth model to observed length-weight data. Top right is the correlation among the slope (a) and power (b) parameters drawn from the variance-covariance matrix of the model fit.

The mean ‘maturity’ (fraction of individuals mature) ogive was calculated from the historical data (all regions combined, Figure 4a) and was the basis for simulating correlated uncertainty in L50 (length at 50% maturity) in Figure 2. A very different maturity ogive is provided by the more recent fishery independent survey (all regional combined, Figure 4b).

Figure 4a. The logistic relationship between length and fraction mature (fraction ‘stage gonad development 3-7’) of the historical data set.

Figure 4b. The logistic relationship between length and fraction mature (fraction ‘stage gonad development’ > 2) of the fishery independent survey data set.

Acknowledgements

Sean Tracey, Nils Krueck, Kate Stark, Alyssa Marshall, Peter Coulson, Barrett Wolfe, Katie Cresswell, Ruth Sharples.

Appendix 1: MSE terminology

Operating models

An operating model is a theoretical description of fishery and population dynamics used for the testing of management strategies that could include, for example, data collection protocols, stock assessment methods, harvest control rules, enforcement policies and reference points. In fisheries, operating models are used in closed-loop simulation to test management procedures (aka. harvest strategy) accounting for feedbacks between the system, data, management procedure and implementation. A management procedure is any codifable rule that calculates management advice from data. Management Strategy Evaluation uses closed-loop simulation of management procedures as a core technical component but is a wider process of stakeholder and manager engagement that identifies system uncertainties, performance metrics, viable management procedures, ultimately aiming to adopt an MP for the provision of management advice for an established time period.

Reference Case Operating Models

The reference case operating model is used as the single ‘base’ operating model from which reference set and robustness set operating models are specified. Reference and robustness tests are typically 1-factor departures from the reference case OM, however sometimes reference set OMs are organized in a factorial grid across primary axes of uncertainty.

Reference Set Operating Models

Reference set operating models span a plausible range of the core uncertainties for states of nature. These are often the types of alternative parameterizations or assumptions that would be included in a stock assessment sensitivity analysis.

The role of the reference set operating models is to provide the central basis for evaluating the performance of candidate management procedures, for example rejecting badly performing harvest strategies.

Robustness Set Operating Models

Robustness set operating models are intended to include additional sources of uncertainty for providing further discrimination among management procedures that perform comparably among reference set operating models.

Robustness operating models often represent system states of nature that are not empirically informed or are hypotheses of a subset of stakeholders.

Developing A Framework for Management Decision Making for the Tasmanian Sand Flathead Fishery

Tom Carruthers tom@bluematterscience.com

08 May, 2024

Disclaimer

Objective

Project details

Current Issues

Study Area

Operating models

Stochasticity in life history characteristics

Meta Data

Data

Software and Code

Recent Presentations

Reports

Research papers

References

Acknowledgements

Appendix 1: MSE terminology

Operating models

Reference Case Operating Models

Reference Set Operating Models

Robustness Set Operating Models